GitHub

Centrum-IntelliPhysics/local-neural-operator-time-stepper-instead-of-just-time-stepper

This repository contains code for the paper: "Enabling Local Neural Operators to perform Equation-Free System-Level Analysis" G. Fabiani, H. Vandecasteele, S. Goswami, C. Siettos, I.G. Kevrekidis ...
Game Rant

Call of Duty: Black Ops 7 Players Can Get a Free Operator Skin, But There's a Catch

Kyle has a degree in Film, Television, and Cultural Studies and has loved video games for as long as he can remember. He's owned every PlayStation, dabbled with the occasional Xbox, and even owned a ...
Scientific Research Publishing

Pazy, A. (1983) Semigroups of Linear Operators and Applications to Partial Differential Equations. Springer-Verlag.

ABSTRACT: In this paper, a spacecraft system is investigated. The system is formulated by partial differential equations with the initial and the boundary conditions. The spectral analysis and ...
GitHub

Solving Ordinary Differential Equations Numerical Method Implementation in Python

Euler Method: The simplest numerical method for solving ODEs, which uses the derivative to project forward. [ y_{n+1} = y_n + h \cdot f(x_n, y_n) ] Heun's Method (Improved Euler Method): A two-step ...
marktechpost

Newton Informed Neural Operator: A Novel Machine Learning Approach for Computing Multiple Solutions of Nonlinear Partials Differential Equations

Neural networks have been widely used to solve partial differential equations (PDEs) in different fields, such as biology, physics, and materials science. Although current research focuses on PDEs ...
INSPIRE

Employing an operator form of the Rodrigues formula to calculate wavefunctions without differential equations

The factorization method of Schrödinger shows us how to determine the energy eigenstates without needing to determine the wavefunctions in position or momentum space. A strategy to convert the energy ...
IEEE

A Solution Method for Differential Equations Based on Taylor PINN

Abstract: Based on deep neural network, elliptic partial differential equations in complex regions are solved. Accurate and effective strategies and numerical methods for elliptic partial differential ...
marktechpost

This Paper Unravels the Mysteries of Operator Learning: A Comprehensive Mathematical Guide to Mastering Dynamical Systems and PDEs (Partial Differential Equation) through ...

The remarkable potentials of Artificial Intelligence (AI) and Deep Learning have paved the way for a variety of fields ranging from computer vision and language modeling to healthcare, biology, and ...
IEEE

Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator

Abstract: In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial ...
Frontiers

Complex solutions for nonlinear fractional partial differential equations via the fractional conformable residual power series technique and modified auxiliary equation method

The fractional-order nonlinear Gardner and Cahn–Hilliard equations are often used to model ultra-short burst beams of light, complex fields of optics, photonic transmission systems, ions, and other ...